docstr: Given examples and logits for those examples,
  compute whether the predicted label is correct for each example
docstr_tok: ["Given","examples","and","logits","for","those","examples",",","compute","whether","the","predicted","label","is","correct","for","each","example"]
code: def _accuracies_from_logits(self, examples, logits) -> list[int]:
      """Given examples and logits for those examples,
      compute whether the predicted label is correct for each example"""
      labels_correct = []
      for i, example in enumerate(examples):
          relevant_logits = self._extract_relevant_logits(
              logits, example, i
          )
          label_correct = int(
              np.argmax(relevant_logits.cpu().detach().numpy())
              == example.answer_index
          )
          labels_correct.append(label_correct)
      return labels_correct
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Union, cast, Sequence
  from typing_extensions import Literal, get_args
  import numpy
  import torch
  import torch.nn.functional
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from accelerate import Accelerator
  from .isp_dataset import ClassificationExample, Example, ExampleWithClasses, LogoddsExample, SequenceProbExample, NumericExample, TaskType
  from .isp_utils import BasicParser
  from .isp_utils import OpenAIModel, OpenaAIModelList
label_desc: Use image node_id 10 for calling the HFModel obj's underlying member method code with example usage: obj._accuracies_from_logits(examples, logits) and returns: labels_correct